Sunday, July 23, 2017

Tutorbots are here - 7 ways they could change the learning landscape

Tutorbots are teaching chatbots. They realise the promise of
a more Socratic approach to online learning, as they enable dialogue between
teacher and learner.

Frictionless learning

We have seen how online behaviour has moved from flat
page-turning (websites) to posting (Facebook, Twitter) to messaging (Txting,
Messenger). We have see how the web become more natural and human. As
interfaces (using AI) have become more frictionless and invisible, conforming
to our natural form of communication (dialogue), through text or speech. The
web has become more human.

Learning takes effort. So much teaching ignores this
(lecturing, long reading lists, talking at people). Personalised dialogue
reframes learning as an exploratory, yet still structured process where the teacher
guides and the learner has to make the effort. Taking the friction and
cognitive load of the interface out of the equation, means the teacher and
learner can focus on the task and effort needed to acquire knowledge and
skills. This is the promise of tutorbots. But the process of adoption will be
gradual.

Tutorbots

I’ve been working on chatbots (tutorbots) for some time with
AI programmes and it’s like being on the front edge of a wave.... not sure if
it will grow like a rising swell on the ocean or crash on to the shore. Yet it
is clear that this is a direction in which online learning will go. Tutorbots
are different from chatbots in terms of the goals, which are explicitly
‘learning’ goals. They retain the qualities of a chatbot, flowing dialogue,
tone of voice, exchange and human (like) but focus on the teaching of knowledge
and skills.

The advantages are clear and evidence has emerged of
students liking the bots. It means they can ask questions that they would not
ask face to face with an academic, for fear of embarrassment. This may seem odd
but there’s a real virtue in having a teacher or faculty-free channel for low
level support and teaching. Introverted students, whom have problems wit social
interaction, also like this approach. The sheer speed of response also matters.
In one case they had to build in a delay, as it can respond quicker than a
human can type. Compare that to the hours, days, weeks it takes a human tutor
to respond. It is clear that this is desirable in terms of research into one to one learning and the research from Nass and Reeves at Stanford confirmed that this transfer of human qualities to a bot is normal.

But what can they teach and how?

1. Teaching support

I’ve written extensively on the now famous Georgia Tech example of a tutorbot teaching assistant, where they swapped out one of their
teaching assistants with a chatbot and none of the students noticed. In fact
they though it was worthy of a teaching award. They have gone further with more
bots, some far more social. Who wouldn’t want the basic administration tasks in
teaching taken out and automated, so that teachers and academics could focus on
real teaching? This is now possible. All of those queries about who, what, why,
where and when can be answered quickly (immediately), consistently and clearly
to all students on a course, 24/7.

2. Student engagement

A tutorbot (Differ) is already being used in Norway to
encourage student engagement.It engages
the student in conversation, responds to standard inquiries but also nudges and
prompts for assignments and action. This has real promise. We know that
messaging and dialogue has become the new norm for young learners, who get a
little exasperated with reams of flat content or ‘social’ systems that are
largely a poor-man’s version of Facebook or twitter. This is short, snappy and
in line with their everyday online habits.

3. Teaching knowledge

Tutorbots, that take a specific domain, can be trained or
simply work with unstructured data to teach knowledge. This is the basic workaday
stuff that many teachers don’t like. We have been using AI to create content
quickly and at low cost, for all sorts of areas in medicine, general
healthcare, IT, geography and for skills-based training using WildFire. Taking
any one of these knowledge-sets, allows us to create a bot that re-presents
that knowledge as semi-structured, personalsed dialogue. We know the answers,
and recreate the questions with algorithmic tutor-behaviours. The tutorbot can
be a simple teacher or assessor. On the other hand it can be a more
sophisticated teacher of that knowledge, sensitive to the needs of that
individual learner.

4. Tutor feedback

Feedback, as explained by theorists such as Black andWilliam, is the key to personalised learning. Being sensitive to what that
individual learners already know, are unsure about or still need to know, is a
key skill of a good teacher. Unfortunately few teachers can do this effectively,
as a class of 30 plus or course with perhaps hundreds of students, means it is
impractical. Tutorbots specialise in specific feedback, trying to educate
everyone uniquely. Dialogue is personal.

5. Scenario-based
learning

Beyond knowledge, we have the teaching and learning of more
sophisticated scenarios, where knowledge can be applied. This is often absent
in education, where almost all the effort is put into knowledge acquisition. It
is easy to see why – it’s hard and time consuming. Tutorbots can pose problems,
prompt through a process, provide feedback and assess effort. Bots can ask for
evidence, even asses that evidence.

6. Critical thinking

As the dialogue gets better, drawing not only on a solid
knowledge-base, good learner engagement through dialogue, focussed and detailed
feedback but also critical thought in terms of opening up perspectives,
encouraging questioning of assumptions, veracity of sources and other aspects
of perspectival thought, so critical thinking will also be possible. Tutorbots
will have all the advantages of sound knowledge to draw upon, with the additional
advantage of encouraging critical thought in learners. They will be able to
analyse text to expose factual, structural or logical weaknesses. The absence
of critical thought will be identified as well as suggestions for improving
this skill by prompting further research ideas, sound sources and other avenues
of thought.

7. General teacher

The holy grail in AI is to find generic algorithms that can
be used (especially in machine learning) to solve a range of different problems
across a number of different domains. This is starting to happen with deep
learning (machine learning). The tutorbot will not just be able to tutor in one
subject alone, but be a cross-curricular teacher, especially at the higher
levels of learning where cross pollination is often fruitful. It will cross-departmental,
cross-subject and cross-cultural, to produce teaching and learning that will be
free from the tyranny of the institution, department, subject or culture in
which it is bound.

Tutornet

As a tutorbot does not have the limitations of a human, in
terms of forgetting, recall, cognitive bias, cognitive overload, getting ill,
sleeping 8 hours a day, retiring and dying - once on the way to acquiring
knowledge and teaching skills, it will only get better and better. The more
students that use its service the better it gets, not only on what it teaches
but how it teaches. Courses will be fine-tuned to eliminate weaknesses, and
finesse themselves to produce better outcomes

Warning

We have to be careful about overreach here. These are not
easy to build, as tutorbots that do not have to be ‘trained (in AI-speak ‘unsupervised’)
are very difficult to build. On the other hand trained bots, with good data
sets (in AI-speak ‘supervised’), in specific domains, are eminently possible –
we’ve built them.

Another warning is that they are on a collision course with
traditional Learning Management Systems, as they usually need a dynamic
server-side infrastructure. As for SCORM – the sooner it’s binned the better.

Concusion

Finally, this at last is a form of technology that teachers
can appreciate, as it truly tries to improve on what they already do. It takes
good teaching as it’s standard and tries to eliminate and streamline it to
produce faster and better outcomes at a lower cost. They are here, more are
coming, resistance is futile!

3 Comments:

Ahead of the curve again....the challenge will be getting policy makers in education to recognise the potential here and engage with teachers....this will mean more not less teachers but with a different skills...thats the challenge Donald..